BKitainge/Tanzania-Water-Wells-Condition-Predictor

Tanzanian Water Wells Dataset # TANZANIA WATER WELLS PROJECT # Project Summary: Building a Classifier to Predict Water Well Conditions Our client is an NGO focused on locating water wells that need repair in order to provide access to clean drinking water in rural areas. The client has provided us with a dataset containing information on various water wells, including their location, construction date, and various physical measurements such as depth and water volume. The goal of this project is to build a classifier that can predict the condition of a water well based on this information, in order to help the client prioritize which wells to repair first. We will begin by exploring the dataset and conducting any necessary data cleaning and preprocessing. We will then perform feature engineering to extract relevant information from the dataset and transform it into a format suitable for modeling. Next, we will train and evaluate several classification models on the dataset, such as logistic regression, decision trees, and random forests, in order to determine which model performs best at predicting water well conditions. Once we have selected the best performing model, we will fine-tune its hyperparameters using techniques such as grid search and cross-validation. Finally, we will use the trained model to make predictions on a holdout dataset and evaluate its performance using metrics such as accuracy, precision, and recall. We will also perform a cost-benefit analysis to determine the potential impact of using the model to prioritize water well repairs. Overall, this project aims to provide our client with a tool that can help them more effectively allocate their resources towards repairing water wells in rural areas, ultimately improving access to clean drinking water for those who need it most. ## Column names 1. date_recorded - The date the row was entered 2. funder - Who funded the well 3. gps_height - Altitude of the well 4. installer - Organization that installed the well 5. longit …